Data source metric visualizations

ABSTRACT

A data intake and query system processes and stores events, which are associated with token identifiers for tokens corresponding to data sources for the messages that the events are generated from. Thus, the data intake and query system can receive a request to provide analyses and visualizations regarding stored events associated with a particular component associated with a plurality of events, such as a data source for the messages from which the plurality of events are generated from. These requests and the resulting visualizations can be customized based on selected tokens and selected components.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/698,810, filed on Nov. 27, 2019, titled “DATA SOURCETOKENS”, which is a continuation of U.S. patent application Ser. No.15/011,652, filed on Jan. 31, 2016, titled “ANALYSIS OF TOKENIZED HTTPEVENT COLLECTOR”, each of which is incorporated by reference herein inits entirety.

BACKGROUND

The proliferation of internet-connected devices has greatly increasedthe number of devices that are acquiring and sending data over networks.Such devices are often operated in varied environments and arefrequently operated by relatively unsophisticated consumers. Thus, manyinternet-connected devices have limited access to a dedicated or complexcommunications infrastructure for purposes of data storage and analysis.These devices may, however, have access to a network such as theinternet. Thus, it may be possible for this large number ofinternet-connected to devices to provide data to a data storage system.

In addition to lacking access to a complex communication infrastructure,many internet-connected devices may operate only a limited number ofapplications, or may have limited processing capability to operateparallel monitoring and logging applications. Adding traditional datalogging functionality to such devices may compromise performance or addsignificantly to development time. Unlike large-scale commercialoperations and complex technical installations, developers of manyinternet-connected devices may have limited time or ability to create orintegrate large-scale data storage functionality in or with theirdevices.

Even if developers of the large number of internet-connected devicescould provide the data that they are gathering to a data storage system,it quickly becomes extremely difficult to process, secure, store, andquery such a large volume of data in a secure manner. With fast-changingtechnologies, configurations that were recently acceptable or ideal maynot function well with the data provided from new and different types ofinternet-connected devices. Because of the large volumes of data and thechanging nature of data provided by internet-connected devices, datastorage systems may be unable to scale across new technologies anddevices.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 illustrates an exemplary global settings user interface inaccordance with some embodiments of the present disclosure;

FIG. 19 illustrates an exemplary token setup and configuration userinterface in accordance with some embodiments of the present disclosure;

FIG. 20 illustrates an exemplary event collector user interface inaccordance with some embodiments of the present disclosure;

FIG. 21 illustrates an exemplary HTTP message utilizing token-basedauthentication and a custom JSON in accordance with some embodiments ofthe present disclosure;

FIG. 22 illustrates an exemplary event collector implementation inaccordance with some embodiments;

FIG. 23 illustrates exemplary steps for receiving, processing, andindexing HTTP data in accordance with some embodiments of the presentdisclosure;

FIG. 24 illustrates exemplary steps for processing acknowledgements inaccordance with some embodiments of the present disclosure; and

FIG. 25 illustrates an exemplary visualization in accordance with someembodiments of the present disclosure.

DETAILED DESCRIPTION

A device may be a data source that generates raw machine data forstorage as events at a data intake and query system. The raw machinedata and event metadata may be provided to one or more event collectorsvia a standard internet protocol such as the Hypertext Transfer Protocol(HTTP). The data intake and query system may include multipleconfiguration interfaces to facilitate the configuration of the system.A global event settings interface may provide global settings formetadata such as source type or index. These global event settings maybe applied to all data that is processed by an installation of thesystem unless those settings are overridden.

The system may also include an interface for the creation andconfiguration of tokens. Tokens are distributed to data sources, and byproviding a token, the device sending the message indicates to the eventcollector that it is authentic. Tokens may be distributed in variousways, for example, such that similar devices share the same token. Thetoken also has its own event settings, including for event metadata.These settings may override the global event settings in the event thatthey conflict.

The data source has a variety of ways to send raw machine to an eventcollector via a HTTP message. In some embodiments, raw machine data maybe sent as raw data, with the HTTP message URI or text including rawmachine data that includes information such as text delimiters, whichmay be used to identify fields and metadata. In some embodiments, a datasource may utilize a custom message format such as a custom JavaScriptObject Notation (JSON). This may include information that identifies rawmachine data and event settings (e.g., metadata) within the text of theHTTP message. The event settings provided in the HTTP message mayoverride both the global event settings and the token event settings.

A data intake and query system may receive requests to analyze theoperation of event collectors and the event collection system. Therequests may specify tokens to be analyzed as well as specificcomponents (e.g., event collectors, forwarders, indexers) to beanalyzed. This analysis may include determining performance metrics,determining statistics, providing analytics, and providingvisualizations. Examples of such performance metrics include CPU-relatedperformance metrics, disk-related performance metrics, memory-relatedperformance metrics, network-related performance metrics, energy-usagestatistics, data-traffic-related performance metrics, overall systemavailability performance metrics, cluster-related performance metrics,and virtual machine performance statistics.

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5 ).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML, documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,emitted “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and in U.S.patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in FIG.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in FIG.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. Patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. This statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4 , or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head aggregates the received results 806 to form asingle search result set. By executing the query in this manner, thesystem effectively distributes the computational operations across theindexers while minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4 , data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. Patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2 , the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

3.1. HTTP Event Collector

As described herein, data may be provided from a variety of datasources. One manner of providing data to a data intake and query systemmay be to provide the data via a protocol such as the hypertext transferprotocol (HTTP). Although the present disclosure may describe providingdata to the data intake and query system via HTTP to create events (HTTPevents), it will be understood that any suitable protocol thatfacilitates communication with servers over a network over the internetmay be implemented in accordance with the present disclosure (e.g.,internet protocol (IP) events). In an embodiment as described herein,the data that is sent may be described as raw machine data. However, itwill be understood that any suitable data strings (e.g., structureddata, unstructured data, raw data, etc.) may be “event data” that may besent via HTTP to create events for storage.

Raw machine data may be provided from any suitable data source withaccess to a network such as the internet (e.g., computers, smart phones,smart watches, connected home devices, vehicles, drones, internet ofthings (IoT) devices, etc.) by embedding raw machine data within HTTPmessages. In some embodiments, the data intake and storage system mayprovide a structured methodology for providing raw machine data andassociated metadata for events via HTTP. Such a structure may allow fordevelopers to quickly and easily create applications that are capable ofproviding events to the system and providing metadata for event storage.

In some embodiments, the HTTP event storage system may provide for atiered system of configuration for system settings. Although it will beunderstood that a tiered system may be provided in any suitable manner,in some embodiments an installation of the system may includeglobal-level settings, token-level settings, and message-level settings.Global event storage settings may apply to an entire installation, andmay provide the defaults that apply to all raw machine data provided tothe data intake and query system for that installation. In someembodiments, some or all of the global event storage settings may beoverridden by other tiers of event settings, such as the token-levelsettings or message-level settings. As is described herein, a userinterface may be provided that allows a user (e.g., a systemadministrator) to assign the global event storage settings remotely, andto update those settings as desired.

In some embodiments, tokens may provide a basis for identifying an HTTPmessage including raw machine data as originating from an authorizeduser of the system. As is described herein, multiple tokens may beauthorized for a single installation of a data intake and storagesystem. Each token may be individually configurable by a user (e.g., asystem administrator), such that each individual token may provideunique event settings that are applicable to HTTP messages includingthat token. In this manner, a user (e.g., a system administrator) iscapable of providing tokens for a variety of purposes, facilitating alarge, scalable system for storing raw machine data from a variety ofdevices operating a variety of applications, all within a singleinstallation of the data intake and storage system. For example, uniquetokens may be provided at any suitable level of a system at whichconfigurability is needed, for example, to different types of devices,devices from different manufacturers, for particular softwareapplications or versions, for each unique device, or at any othersuitable level at which configuration is desired. In an embodiment of atiered HTTP event storage system, some or all of the token-level eventsettings may override global event settings, such that a user (e.g., asystem administrator) may utilize tokens to provide customizedconfigurations as desired.

Once the data intake and query system and tokens associated with thesystem are initialized and configured, raw machine data may be providedto the system by devices via HTTP messages. Although raw machine data beprovided in any suitable manner, in some embodiments raw machine datamay be provided in a variety of manners formats, including as raw data(e.g., sent as a message payload or provided within a URI) or within astructured messaging format. In some embodiments, a structured messagingformat may include a custom JavaScript Object Notation (JSON) thatprovides a standardized format for providing raw machine data and eventsettings to the data intake and query system, as described in moredetail herein. A developer may utilize any of these methods to createexecutable code and applications that provide raw machine data to thedata intake and query system. In some embodiments, raw machine data maybe provided as both raw data and within the custom JSON, allowingdevelopers flexibility in creating applications that provide event data.

The data intake and query system may include a variety of components forreceiving and processing raw machine data received via HTTP messages. Insome embodiments, the system may also process data received from avariety of other sources in addition to HTTP messages as describedherein, providing an integrated system for storage of a large variety ofraw machine data from different types of data sources. The data intakeand query system may be highly scalable, and may include any suitablestorage components, as discussed herein, to support the intake andquerying of events generated from raw machine data, including eventsgenerated based on raw machine data provided in HTTP messages (i.e.,HTTP events). In some embodiments, one or more event collectors may beprovided for facilitating intake and querying of events generated basedon raw machine data provided in HTTP messages. An event collector may beimplemented at any suitable level of the data intake and query system(e.g., at forwarders, indexers, etc.) and may include any other suitablecomponents such as traffic load balancers, deployment servers, etc.

Although it will be understood that an event collector may processincoming raw machine data from an HTTP message in any suitable manner,in some embodiments, the event collector may identify a token associatedwith the raw machine data (e.g., based on an identifier provided in theHTTP message). The token may provide for authentication of the datasource within the data intake and query system. Global event settingsmay be accessed for the system. In an embodiment where the token hasbeen configured to have token event settings that override the globalevent settings, the token event settings may also be accessed (e.g.,from storage at the event collector). The underlying HTTP message may beprocessed to identify raw machine data and any message event settings(e.g., metadata) provided within the HTTP message. As described herein,in some embodiments the message event settings may override the globalevent settings and token event settings, providing a developer theability to specify settings at a variety of degrees of granularity,depending on application. Once the raw machine data and settings (e.g.,metadata) have been accessed, the event (i.e., an HTTP event) may begenerated and stored (e.g., at an indexer). In some embodiments, atimestamp may be provided with raw machine data within the HTTP messageor the raw machine data itself. If a timestamp is not identified fromthe HTTP message, one may be assigned to the raw machine data when theevent is generated.

In some embodiments, it may also be desired to provide information backto the device that sent the HTTP message. Although any suitableinformation may be provided in accordance with the present disclosure,in some embodiments, the event collector may send an HTTP messageincluding an acknowledgement regarding a generated event. While anacknowledgement may provide any suitable information regarding agenerated event, in some embodiments an acknowledgement may allow adevice to determine whether the generated was stored (e.g., at anindexer). In some embodiments, an event collector may simply provide anacknowledgement indicating when a message has successfully been stored(e.g., a synchronous acknowledgement message). In some embodiments, anacknowledgement message may include information such as an identifierthat allows a device to query the event collector at a later time todetermine whether a generated event was successfully indexed.

In some embodiments, the event collector and device may establish aunique channel for acknowledgments back to the device that sent the HTTPmessage. Although a unique channel may be provided in any suitablemanner, in an embodiment the unique channel may be a global uniqueidentifier (GUID) for a particular channel. In some embodiments, aunique channel may assist in preventing an attacker from accessing orstealing acknowledgments. For example, when a device sends an HTTPmessage including raw machine data to the event collector, it mayinclude the channel GUID with the message and may send the messagesecurely (e.g., using HTTPS). A plurality of client devices that havethe same token will have different channel GUIDs. Thus, each of theserver and client may only respond to acknowledgement communicationsthat include a correct channel GUID. This may assist in preventingcertain types of attacks, such as spoofing of acknowledgement messagesor sending numerous requests including bogus data (e.g., to slow downprocessing by the data intake and query system).

In some embodiments, a device sending HTTP event requests, a data intakeand query system (e.g., an event collector), or both collectively, mayestablish and apply limits to acknowledgement or other activities inorder to thwart attacks (e.g., malicious attacks such asdenial-of-service (DOS) attacks). For example, limits may be establishedfor parameters such as the total number or frequency of acknowledgementrequests, channels, and acknowledgement requests per channel. If anysuch limit is exceeded, a server such of a data intake and query system(e.g., an event collector) may not process a request related to anacknowledgement (e.g., by not processing the request and returning aserver busy message to a device).

In some embodiments, the data intake and query system may also storedetailed metrics regarding operations of an HTTP event collector system.In some embodiments, these metrics may be logged and stored (e.g., in alog file) which may be updated (e.g., periodically updated) based onsystem or administrator settings. This logged data may then be accessed(e.g., by an administrator) and analyzed (e.g., generating analysis,statistics, and visualizations as described herein) in order to monitorthe operation of the system. Although any suitable metrics may belogged, in an embodiment the data intake and query system may includemetrics that are logged system-wide (e.g., for an installation orinstance) and metrics that are logged for each token.

Although any suitable system-wide metrics may be logged and accessed,exemplary system-wide metrics may include a number of authenticationerrors due to an invalid token, a total number of per-token errors(e.g., due to multiple causes such as improper data format, noauthorization request, a failure of an authorization request,connectivity failures, etc.), a total number of per-token eventsreceived by each HTTP event collector endpoint, a total number ofper-token parser errors as a result of improperly formatted event data,a total number of per-token individual HTTP requests received by eachHTTP event collector endpoint (e.g., including HTTP requests thatprovide information for multiple events), a total number of requests toan incorrect URL, a total number of requests from certain programs orsystems (e.g., a total number of requests from a SPLUNK MobileINTelligence (MINT) system), a total number of per-token requests todisable a token, information about types of metrics data, a total amountof per-token data (e.g., in bytes) sent to the indexer, a total amountof per-token data (e.g., in bytes) received by each endpoint, logseverity information, data transport protocol information for collectedevents, and date and time information for collected events.

Although any suitable per-token metrics may be logged and accessed,exemplary per-token metrics may include a total number of per-tokenerrors (e.g., due to multiple causes such as improper data format, noauthorization request, a failure of an authorization request,connectivity failures, etc.), a total number of events received for thetoken, a total number of parser errors as a result of improperlyformatted event data for the token, a total number of individual HTTPrequests received by the token (e.g., including HTTP requests thatprovide information for multiple events), a total number of request toan incorrect URL for the token, a total number of requests from certainprograms or systems (e.g., a total number of requests from the SPLUNKMobile MINT system) for the token, a total number of per-token requeststo disable the token, information about types of metrics data, a totalamount of data (e.g., in bytes) sent to the indexer for the token, atotal amount of data (e.g., in bytes) received for the token, logseverity information, data transport protocol information for collectedevents, and date and time information for collected events.

In some embodiments, the data intake and query system may also provideanalytics and visualizations to a user (e.g., a system administrator) toallow the user to determine information about events being stored withinthe system (e.g., events stored at indexers) and the operation of thesystem (e.g., logged metrics for the system), or a combination thereof.Data, analyses, statistics, performance metrics, and other informationmay be used to generate the visualization. Any suitable analysis andvisualizations may be provided for HTTP events, as described herein.Analytics and visualizations may be provided for the entire system, forparticular tokens, and for certain components of the data intake andquery system (e.g., event collectors, forwarders, and indexers). In thismanner, a user may be allowed to monitor information about events beingstored at the system, about the operation of the system (e.g.,throughput, amount of data, etc.), and about their products and systems(e.g., based on different tokens associated with different types ofdevices providing events to the data intake and storage system).

3.1.1. Global Event Settings

As described herein, a user (e.g., system administrator) may be able toconfigure global settings for the HTTP event collector of the dataintake and query system. Global settings may be used to configure anysuitable information about the operation of the system and the devicesproviding data to the system, including information about how events aresubmitted via HTTP messages and processed by the system (e.g., whetherto use a deployment server, whether to enable secure socket layer (SSL)encryption, HTTP port number, whether to use tokens, etc.) andinformation about metadata for raw machine data provided to the system(a default source type, default index, default output group, etc.).

FIG. 18 depicts an exemplary global settings user interface inaccordance with some embodiments of the present disclosure. Although aglobal settings user interface may include any suitable information, insome embodiments, a global settings user interface may allow a user(e.g., system administrator) to configure information relating totokens, a default source type, a default index, a default output group,whether to use a deployment server, whether to enable SSL, and an HTTPport number. Although the global settings interface depicts certain userinterface elements (e.g., radio buttons, pull-down menus, text entryboxes), it will be understood that any suitable user interface elementsmay be utilized with a global settings interface in accordance with thepresent disclosure, and that any suitable setting may be associated withany suitable user interface element.

Token selection interface 1802 allows a user to selectively enable ordisable the tokens associated with the installation of the data intakeand query system. In some embodiments, it may be desired to temporarilydisable tokens, which may effectively disable the logging of events tothe system. For example, it may be desired to distribute and deploy newtokens or software to devices that are providing data to the system. Asdescribed herein, a data intake and query system enables analytics thatmay cause a user to identify errors, attacks, or other issues that areoccurring within their systems. In some embodiments, it may be desirableto disable tokens once an error or attack is identified.

Default source type selection interface 1804 allows a user to select adefault source type that will be applied to all raw machine data unlessotherwise specified (e.g., using token event settings or message eventsettings). In some embodiments, the source type selections available asdefaults may be provided from a listing of known source types, which mayindicate a known data structure for events, which may be used for dataintake and querying. In addition, a customer source type may be defined,and in some embodiments, provided as an option for the default sourcetype.

Default index selection interface 1806 allows a user to select a defaultindex that raw machine data will be stored in by the data intake andquery system. The selection of an index may provide the location whereevents including raw machine data, searchable data, and metadata arestored within the data intake and query system. By selecting amongindexes, the user may be able to balance the storage, processing, andsearch demands on different components of the system.

Default output group selection 1808 allows a user to define a defaultgroup of indexers (the output group) that is to store events for aparticular installation. In some embodiments, available output groupsmay be provided based on the indexers that are associated with theselected index. In some embodiments, one or more output groups may becustomized by a user, and available for default selection.

Deployment server selection interface 1810 may allow the user todetermine whether a deployment server will be used for the installation.In some embodiments, a deployment server may be an instance that acts asa centralized configuration manager for managing a set of eventcollectors, forwarders, and indexers. The deployment server may downloadupdated content, such as configuration files and apps, to the manageddevices. Deployment server selection interface 1810 may allow a user todetermine whether a deployment server will be utilized.

Enable SSL selection interface 1812 may allow a user to enable SSL forHTTP messages that provide raw machine data to the system. Although SSLencryption may be specified in accordance with the global settingsinterface of FIG. 18 , it will be understood that other forms ofencryption (e.g., transport layer security (TLS)) may be used, and insome embodiments, an encryption selection interface 1812 may facilitatea selection between different encryption options.

HTTP port number selection 1814 may provide an input (e.g., a textinput) that allows a user to define a default HTTP port number. In someembodiments, a set of default selections may be provided by the system,and an option may be provided for the port number to be user-assigned.

Cancel selection 1816 and save selection 1818 allow a user to save orcancel default settings. When the save button is selected, the updatesettings may by transmitted to the system and saved.

3.1.2. Token Setup and Configuration

A plurality of tokens may be set up for a particular installation of thedata intake and query system. Each token may be individuallyconfigurable, and a variety of aspects of the token may be configurable,such as any suitable metadata settings, messaging settings for providingHTTP messages to the system, and any other suitable configuration of thesystem. By setting up a variety of tokens, it may be possible to managethe manner in which disparate data sources provide events to the systemand how those events are stored an accessed. As described herein, tokensmay be individually analyzed, allowing for a granular-level analysis ofparticular data sources. Moreover, security features may be realized,for example, by configuring tokens in different manners (e.g., storingevents in a different manner or at certain indexers) based on differentusers or user types (e.g., as established by possession of a particulartoken).

FIG. 19 depicts an exemplary user interface for setting up andconfiguring a token in accordance with some embodiments of the presentdisclosure. Although particular information as depicted as beingconfigured for a token in FIG. 19 , it will be understood that anysuitable information may be configured for a token, including anymetadata, any event storage requirements, and data communicationparameters (e.g., encryption, etc.), any other suitable information, orany combination thereof. For example, a token may be configured toinclude metadata for data sources and indexes, and in some embodiments,provide a timestamp for data (e.g., a token could be associated withdata sources that do not natively provide timestamps for events).

Name user input 1902 may allow a user to define a name of a token, whiledescription user input 1906 may allow a user to define a description forthe token. As will be described herein, in some embodiments a tokenidentifier may be provided for identifying the token at both the datasource and the event collector. A token name and description mayutilized by a user (e.g., system administrator) to provide a short-handdescription for tokens. For example, in an implementation of tokenizedHTTP event collection in a home automation system, different types ofdevices could be assigned different tokens, and the name and descriptionprovided within the token setup and configuration interface may providea shorthand for understanding the application or use of the token. Insome embodiments, a token name may be used for storage and access ofper-token metrics.

Source name override 1904 may provide an override of the default sourcetype for the system. For example, a source type indicated in source nameoverride selection 1904 of FIG. 19 may override a source type defined indefault source type selection 1804 of FIG. 18 . In some embodiments,source name override 1904 may provide for selections that are available,and may indicate the source type that is currently set as the defaultwithin the global settings.

Output group selection 1908 may allow a user to select an output groupto receive events generated based on HTTP messages that include thetoken. In some embodiments, this may allow an override of a defaultoutput group (e.g., an output group specified in output group selection1808 of FIG. 18 ). In some embodiments, output group selection 1908 mayprovide for selections that are available, and may indicate the outputgroup that is currently set as the default within the global settings.

Enable indexer acknowledgement selection 1910 allows a user to specifythat an event collector should provide acknowledgement messages forevents generated based on HTTP messages provided for a particular token.As is described herein, in some embodiments an event collector maycommunicate with a data source to indicate whether raw machine datareceived within HTTP messages have been indexed. In some embodiments,providing acknowledgements may result in additional processing andmessaging overhead, and in some embodiments users may wish toselectively enable or disable acknowledgements.

Next selection 1912 may result in the token settings being saved for thetoken. Once a token is created, a token identifier may be generated. Thetoken identifier may be stored at the event collector, such that savedsettings may accessed for purposes of handling events provided in HTTPmessages.

FIG. 20 illustrates an exemplary event collector user interface inaccordance with some embodiments of the present disclosure. Althoughparticular user interface elements are depicted in FIG. 20 , it will beunderstood that any suitable user interface elements may be provided foran event collector in accordance with the present disclosure. Asdepicted in FIG. 20, an event collector includes user interface elementsthat allow a user to create, view, and modify event collector settings,including settings for global events and tokens.

Token filter field 2002 allows a user to search for tokens that areassociated with a particular installation of an event collector.Although FIG. 20 depicts only three tokens, in some embodiments, anevent collector may have hundreds or even thousands of tokens associatedtherewith. Although token filter field 2002 depicts a text search box,it will be understood that any suitable search mechanism may be providedin accordance with the present disclosure. In some embodiments, alisting of available tokens may be dynamically updated as the userenters the search criteria into the filter. Although the token filterfield 2002 provides for searching of the token name field, the tokenfilter could be applied to any searchable field of the filter.

Global settings selection 2004 may allow the user to access the globalsettings user interface (e.g., of FIG. 18 ) to modify global settingsfor the event collector installation. New token selection 2006 may allowthe user to access a token setup and configuration interface (e.g., ofFIG. 19 ) to create a new token. In some embodiments (not depictedherein), new token selection 2006 may provide for the selection of tokentemplates as a starting point for creation of the new token.

FIG. 20 depicts a variety fields that provide a view of selectedsettings and parameters for tokens that are associated with an eventcollector. Although particular fields are depicted in FIG. 20 , it willbe understood that any suitable additional fields may be added, that anyof the fields may be modified, and that any of the fields may bedeleted. Any of the fields may be searched, ordered, and filtered in anysuitable manner (e.g., by selecting a field heading, and requestingascending or descending order for the display).

Token name field 2008 provides the token name for each of the token(e.g., as specified in the token setup and configuration user interfaceof FIG. 19 ).

Action field 2010 allows a user to select actions to perform with thetoken. A selection to edit a token may result in the opening of a userinterface to modify token settings (e.g., the setup and configurationinterface of FIG. 19 ), allow editing within the fields of the eventcollector user interface, or facilitate editing of tokens in any othersuitable manner. A selection to disable a token may cause the token todisable, such that events associated with a token are not indexed, andin some embodiments, are discarded. The token status field 2018 may beupdated based on a selection to enable or disable a token.

Token value field 2012 may depict token unique identifiers for tokens,which may be used by event collector to authenticate data sources andaccess stored information (e.g., metadata) that is associated with atoken.

Token source type field 2014 and token index field 2016 may depict tokensettings for source type and index, which, as described herein, may beindependently configurable for each token.

3.1.3. Sending Raw Machine Data to the Event Collector

Once a data source is configured, it may provide raw machine data to theevent collector such that events may be generated and indexed. Asdescribed herein, configuration of a data source may include configuringglobal settings and tokens. In some embodiments, once theseconfigurations are complete, the event collector may accept raw machinedata from a data source.

Developers are provided with a variety of options for providing rawmachine data to the configured event collector. In some embodiments, rawmachine data and event settings may be provided within a uniformresource identifier (URI), as raw data, within a custom JSON, usinglogging libraries (e.g. Logback, Log4j2, java.util.logging, SLF4J,etc.), in any other suitable manner, or any combination thereof. Certainmethods of providing raw machine data to the event collector (e.g.,embedding raw machine data and settings within a URI or in a raw datapayload) may simply embed the raw machine data and metadata within anHTTP message. Such methods may require analysis by the event collector(e.g., as described herein for any other suitable raw machine data) forparsing the raw machine data and indexing events generated for the rawmachine data. For example, in some embodiments it may be determinedwhether a message event format for message event settings is presentwithin the HTTP message. In some embodiments, a token must be providedwith the raw data in order to authorize the data source and determineglobal and token settings.

Other methods of providing raw machine data to the event collector(e.g., using a custom JSON or logging library) may provide tools toassist a developer in providing raw machine data and event settings tothe event collector. An exemplary custom JSON or logging library mayinclude key/value pairs that may have a message event format that allowsfor the raw machine data and event metadata to be specified. Althoughany suitable key/value pairs may be supported in accordance with thepresent disclosure, exemplary keys include a time key, a host key, asource key, a sourcetype key, an event key (e.g., for the raw machinedata) and an index value. Values may be assigned to each of these keysduring operation of a data source and provided to the event collectorfor processing and indexing. In some embodiments, providing a valuewithin the custom JSON or logging library may override a default settingprovided within one or both of the global settings (e.g., a sourcetypevalue or index value). A time value may provide a timestamp for the rawmachine data. Although a time value may be provided in any suitablemanner, in some embodiments the time value may provide seconds andmilliseconds in epoch time format. A host value may include informationsuch as a hostname of the client that is sending the raw machine data,while a source value is user-assigned for the raw machine data, and mayrelate to any suitable information such as an application that isproviding the raw machine data.

FIG. 21 depicts an exemplary HTTP message utilizing a custom JSON inaccordance with some embodiments of the present disclosure. Although anexemplary HTTP message may include any suitable components in accordancewith the present disclosure, in an exemplary embodiment a HTTP messageincludes a destination endpoint 2102, an authorization header 2104, anda plurality of raw machine data fields 2106 and 2018 implemented withinthe custom JSON format.

A destination endpoint 2102 may provide the destination of the eventcollector that will process the received HTTP message. Authorizationheader 2104 may provide authorization information for determiningwhether a data source is authorized to transmit events to the eventcollector for indexing. Although any suitable information may beutilized to determine the authorization, in an embodiment theauthorization header may include the token identifier, as describedherein.

Raw machine data fields 2106 and 2108 may be provided in the custom JSONformat. Although any suitable events and event metadata may be providedin the custom JSON, an exemplary first event 2106 may include keys“event,” “time,” and “host,” having associated values “event3”,1450127100, and “fool”. Similarly, a second event 2106 may include keys“event,” “time,” and “host,” having associated values “event4”,1450127194, and “foo2”.

3.1.4. Processing and Indexing of Raw Machine Data

HTTP messages including tokens, raw machine data, and event metadata aretransmitted to an event collector over a network. A system for indexingand querying the received data may be configured in any suitable mannerbased on issues such as the volume and complexity of raw machine datathat will be provided to the system. In some embodiments, an eventcollector may be implemented on a single forwarder, which may thenforward events generated events to a plurality of output groups, eachincluding a plurality of indexers. In some embodiments, event collectorsmay be implemented at indexers. In more complex configurations,additional components such as a traffic load balancer and deploymentserver may be provided as separate components to manage large volumes ofdata and more complex management tasks.

FIG. 22 illustrates an exemplary event collector implementation inaccordance with some embodiments of the present disclosure. Although itwill be understood that event collectors may be implemented in anysuitable manner in accordance with the present disclosure, in anexemplary embodiment event collectors may be implemented at each of aplurality of forwarders, in a system that further includes a loadbalancer and deployment server. FIG. 22 depicts communications thattransmit raw machine data (e.g., HTTP messages, raw machine data, eventmetadata, and generated events) as solid lines and other communicationswith dashed lines.

Devices 2202 a-2202 e may be any suitable devices that are capable ofprocessing information to generate raw machine data and transmit rawmachine data over a network. Although five devices are depicted it willbe understood that the data intake and query system may be configured tohandle any suitable number of devices generating any suitable amount ofraw machine data for storage. Devices 2202 a-2202 e may includeinstructions stored in memory, that when executed by a processor, causeHTTP messages to be generated as described herein (e.g., including atoken identifier, raw machine data, and event metadata provided in acustom JSON). The HTTP messages are transmitted over a network 2204,which may be any suitable network or combination thereof (e.g., theinternet, WiFi, cellular, mesh networks, etc.). The destination endpointspecified in the HTTP messages may route the messages to load balancer2206 via the network.

Load balancer 2206 may receive the HTTP messages and use traffic loadbalancing to route them to a plurality of event collectors. Although anysuitable load balancer may be implemented in any suitable manner, in anexemplary embodiment a load balancer such NGINX may distribute messagesbetween event collectors at regular intervals.

Deployment server 2208 may provide for centralized configuration andmanagement of the data intake and query system, for example, bydistributing configuration settings through the deployment server 2208.In this manner, a plurality of event collectors (e.g., implemented onforwarders 2210 a-2210 c in FIG. 22 ) may be utilized to process, index,and query large volumes of data.

In one embodiment, each of forwarders 2210 a-2210 c may function as aheavy forwarder, performing both forwarding functions and processing ofincoming HTTP messages received from load balancer 2206. Raw machinedata, event metadata, and events generated therefrom may be distributedby the forwarders 2212 a-2212 c, in any suitable manner, and in someembodiments may be distributed to each available indexer.

FIG. 23 illustrates exemplary steps for receiving, processing, andindexing of raw machine data received in HTTP messages in accordancewith some embodiments of the present disclosure. The steps depicted byFIG. 23 are provided for illustrative purposes only; those skilled inthe art will understand that additional steps may be included, that ormore steps may be removed, and that the ordering of the steps of FIG. 23may be modified in any suitable manner. It will be understood that whileparticular hardware, software, system components, HTTP messages, andprocessing and indexing techniques may be described in the context ofFIG. 23 , that the steps described herein are not so limited. Althoughthe steps of FIG. 23 are provided in the context of HTTP messages, itwill be understood that similar steps may be applicable to messages sentusing different protocols.

At step 2302, an event collector (e.g., an event collector running onany one of forwarders 2210 a-2210 c) may receive a HTTP message (e.g.,from devices 2202 a-2202 e via network 2204 and 2206). Once the messageis received at step 2302, processing may continue to step 2304.

At step 2304, the event collector may identify a token from the HTTPmessage. Although a token may be identified in any suitable manner, inan exemplary embodiment a token identifier may be provided within anauthorization header (e.g., the token identifier of authorization header2104). The event collector may also determine whether the token isauthorized based on the provided token information. Once the token isidentified, processing may continue to step 2306.

At step 2306, the event collector may access global event settings.Although global event settings may be accessed in any suitable manner,in some embodiments the global event settings may be accessed once thetoken is confirmed to be valid and enabled (e.g., based on token setupand settings as described with respect to FIGS. 19-20 ). The globalsettings may include default metadata for events, for example, as wasdescribed herein with respect to FIG. 18 . Once global settings havebeen accessed, processing may continue to step 2308.

At step 2308, the event collector may access token event settings.Although token event settings may be accessed in any suitable manner, insome embodiments the token event settings may be accessed if tokens areenabled within the global settings (e.g., as based on settings providedin the global event settings user interface of FIG. 18 ) and the tokensettings (e.g., based on settings provided in the token event settingsinterface of FIGS. 19-20 ). The token event settings may includesettings that override global event settings, based on based on settingsprovided in the token event settings interface of FIGS. 19-20 .Processing may then continue to step 2310.

At step 2310, the event collector may process message event informationreceived within the HTTP message, e.g., within a URI of the HTTP messageor via a custom interface such as a custom JSON (e.g., a custom JSON asdepicted in FIG. 21 ) or log file. In some embodiments, it may bedetermined whether message event information is being received based ona determination that data having a proper format (e.g., a message eventformat) is being provided within the HTTP message. For example, it maybe determined whether raw machine data and metadata may be extractedfrom the JSON as described with respect to FIG. 21 . The message eventsettings metadata may override both global event settings and tokenevent settings. Once the event information has been processed,processing may continue to step 2312.

At step 2312, the event collector may process data provided in rawformat via the HTTP message (e.g., information provided in a raw messageor URI), extracting raw machine data and event settings, as describedherein. Once the raw format information has been processed, processingmay continue to step 2314.

At step 2314, the event collector may determine the final metadata to beincluded with the raw machine data, based on the tiered event settingshierarchy as described herein. For example, message event settings mayoverride token event settings, which override global event settings.Once the final metadata has been determined, processing may continue tostep 2316.

At step 2316, the event may be generated based on the raw machine dataand the metadata, and event may be indexed and stored (e.g., at anindexer). Processing of the steps of FIG. 23 my then end.

3.1.5. Processing of Acknowledgements

FIG. 24 illustrates exemplary steps for processing acknowledgements inaccordance with some embodiments of the present disclosure. The stepsdepicted by FIG. 24 are provided for illustrative purposes only; thoseskilled in the art will understand that additional steps may beincluded, that or more steps may be removed, and that the ordering ofthe steps of FIG. 24 may be modified in any suitable manner. It will beunderstood that while particular hardware, software, system components,HTTP messages, and acknowledgement techniques may be described in thecontext of FIG. 24 , that the steps described herein are not so limited.Although the steps of FIG. 24 are provided in the context ofacknowledgements for events received via HTTP messages, it will beunderstood that similar steps may be applicable to messages sent usingdifferent protocols. Moreover, the steps of FIG. 24 disclose anacknowledgement procedure whereby the data source receives an identifierand polls the event collector to determine the status of theacknowledgement of indexing. It will be understood that the presentdisclosure contemplates other procedures, such as sending a positiveacknowledgement from the event collector once an event is indexed (e.g.,a synchronous acknowledgement). As depicted in FIG. 24 , certain stepsare performed by a client device (C) and certain steps are performed bya server (S) such as an event collector or other component of a dataintake and query system.

At step 2402, a data source (e.g., such as a device 2202 a-2202 e) mayrequest an acknowledgement. Although an acknowledgement may be requestedin any suitable manner, in one embodiment a request for anacknowledgement may be included with the HTTP message that includes theraw machine data and event metadata. The event may be generated andtransmitted to an event collector (e.g., a forwarder 2210 a-2210 c, vianetwork 2204 and load balancer 2206) for eventual storage at one ofindexers 2212 a-2212 c. Processing may then continue to step 2404.

At step 2404, the event collector (e.g., a forwarder 2210 a-2210 c) maygenerate an acknowledgement identifier that is associated with theevent. Although any suitable identifier may be generated, in someembodiments acknowledgement identifiers may be consecutively numberedfor events processed by a forwarder or forwarder to an indexer. Theevent collector (or in some embodiments, an indexer) may store theacknowledgment identifier, an identifier for the event, and informationabout the status of the event, such as a positive indicator that theevent was indexed. Processing may then continue to step 2406.

At step 2406, the event collector (e.g., a forwarder 2210 a-2210 c) mayprovide the acknowledgement identifier to the data source (e.g., adevice such as 2202 a-2202 e) that provided the event (e.g., via loadbalancer 2206 and via network 2204). In some embodiments, the datasource may store the acknowledgement identifier (e.g., within a table,database, memory, or other data store) for future use in checking thestatus of the acknowledgement. Once the acknowledgement identifier isreceived and stored by the device, processing may continue to step 2408.

At step 2408, the data source (e.g., as a device such as 2202 a-2202 e)may transmit a request to check the event status to the event collector(e.g., to one of forwarders 2210 a-2210 c via network 2204 and via loadbalancer 2206). Although the request may include any suitableinformation, in some embodiments the request may include theacknowledgement identifier. Once the status has been requested,processing may continue to step 2410.

At step 2410, the event collector (e.g., one of forwarders 2210 a-2210c) may check the status of the event at the indexer. In someembodiments, the indexer may provide an indication to the eventcollector (or in some embodiments, may implement the event collector)that indicates when an event has been indexed. In other embodiments, anindexed status may be requested, or it may simply be determined whetherthe event is available from the indexer. In some embodiments, the eventcollector may receive either a positive indication that the event hasbeen indexed, or a negative indicator. In some embodiments, the negativeindicator may not provide information that the event has not beenindexed, but only information that it is unknown whether the event wasindexed. Once the status of the event at the indexer has been checked,processing may continue to step 2412.

At step 2412, the event collector (e.g., one of forwarders 2210 a-2210c) may send an acknowledgement status message to the data source (e.g.,a device such as 2202 a-2202 e via network 2204 and via load balancer2206). The acknowledgement status message may include the indicator thatwas determined at step 2410 as well as the identifier for the event.Once the acknowledgement message is received, processing may continue tostep 2414.

At step 2414, the data source may determine from the received messagewhether the acknowledgement was received. If the acknowledgement wasreceived (e.g., a positive indication that the event was indexed),processing may continue to step 2416. If the acknowledgement was notreceived (e.g., an indication that it is not known whether the event wasindexed), in some embodiments, processing may continue to step 2408, atwhich the data source continues to check for the event. In someembodiments (not depicted in FIG. 14 ), if a timeout has expired and theevent is not received, the data source may take corrective action suchas sending the HTTP message including the raw machine data a secondtime. In additional embodiments (not depicted in FIG. 14 ) where anegative indicator means that it is not known that the message was notreceived, the data source may take corrective action such as sending theHTTP message including the raw machine data a second time.

At step 2416, both the data source and the event collector may updatetheir records, for example, by clearing the event from a listing ofunacknowledged events, clearing indicators or flags relating to theevent and acknowledgement, or taking any other suitable action. Oncerecords have been updated at the data source and event collector,processing may end.

3.1.6. Analysis and Visualization of HTTP Event Collection

FIG. 25 illustrates an exemplary visualization of HTTP event collectionin accordance with some embodiments of the present disclosure. Asdescribed herein, a data intake and query system may perform varioustypes of analysis, statistics, and visualizations. An HTTP eventcollector may provide a powerful tool for providing such analysis fordisparate and distributed data sources, based on individual tokenidentifiers, event collectors, indexers, forwarders, and groupingsthereof. In an embodiment, the visualization may be based on dataregarding events stored at indexers as well information related to thedata intake and query system (e.g., based on metrics for the data intakeand query system and tokens, as described herein). This data andinformation, analyses thereof, statistics based on this data, andperformance metrics related thereto may be referred to as visualizationdata, which may be accessed or calculated for the purpose of generatingvisualizations.

An exemplary visualization is provided in FIG. 25 . In the visualizationof FIG. 25 , selection menus 2502 are provided. Selection menus 2502 mayinclude any suitable user interface elements that allow selection orentry of various options, such as pull-down menus, search boxes, radiobuttons, listings of selectable options, or any combination thereof. Theselection menus 2502 may allow users to select data types, analyses,statistics, and visualizations, which determine the type ofvisualization data that is received and utilized for the depiction ofthe visualizations (e.g., in windows 2504-2506).

One exemplary data type that may be analyzed is an analysis of datastorage components, such as indexers, event collectors, or forwarders.For example, a selection menu 2502 may provide options to select some orall data storage components for analysis, or categories (e.g., indexers,event collectors, forwarders, or any combination thereof) of datastorage components. Visualization data related to the data storagecomponents may provide information regarding overall system volume,peaks and spikes in usage of underlying data sources, times when thedata storage components collectors are becoming overloaded or receivingbad data, and various other parameters. In some embodiments it may bedesired, for one or more data storage components, to determine apercentage of events that were successfully received, of HTTP messagesthat were authorized, a percentage of HTTP messages that were improperlyformatted, an amount of data that was received, and other information asdescribed herein. As another example, comparing the operation ofdifferent data storage components may provide information about systemsetup and loading. As a result, system setup (e.g., token indexsettings) may be appropriately modified.

Another exemplary data type that may be analyzed is tokens. A selectionmenu 2502 may allow the selection of some or all tokens to be analyzed.As described herein, different tokens may be created and assigned toparticular devices for numerous reasons, including to provide monitoringof levels of activity of devices assigned different tokens. In oneexemplary embodiment, tokens may be assigned to different device types,or for similar devices deployed to different customers. By monitoringthe volume of events, content of events, error rates, and similarinformation, it may be possible to determine granular performanceinformation on a large scale, all based on the ability to monitorparticular tokens. In some embodiments it may be desired, for one ormore tokens, to determine a percentage of events that were successfullyreceived, of HTTP messages that were authorized, a percentage of HTTPmessages that were improperly formatted, an amount of data that wasreceived, and other information as described herein.

In some embodiments, various data types may be combined, for example tomonitor how particular tokens are impacting particular event collectors.In some embodiments, data types may be viewed over multiple customers,for example to analyze operations of related companies and businesses.

Although it will be understood that any suitable visualization data maybe determined for the purposes of generating visualizations, in someembodiments the visualization data may include event collector metrics,token metrics, events, event fields, event values, event counts,acknowledgements, percentage utilization, underlying event data,CPU-related performance metrics, disk-related performance metrics,memory-related performance metrics, network-related performance metrics,energy-usage statistics, data-traffic-related performance metrics,overall system availability performance metrics, cluster-relatedperformance metrics, virtual machine performance statistics, anystatistics or analysis relating to any of the above, or any combinationthereof. Any suitable visualizations may be provided based on this dataas are described herein (e.g., bar charts, scatter plots, area charts,line charts, pie charts, radial gauges, marker gauges, or fillergauges). For example, data may be analyzed for particular (e.g.,selectable) time periods, as time series data, as aggregated data, toidentify any suitable changes in data over time (e.g., comparisons tothresholds, statistical analysis, etc.), any other suitable analysis, orany combination thereof.

The foregoing provides illustrative examples of the present disclosure,which are not presented for purposes of limitation. It will beunderstood by a person having ordinary skill in the art that variousmodifications may be made by within the scope of the present disclosure.It will also be understood that the present disclosure need not take thespecific form explicitly described herein, and the present disclosure isintended to include variations to and modifications thereof, consistentwith the appended claims. It will also be understood that variations ofthe systems, apparatuses, and processes may be made to further optimizethose systems, apparatuses, and processes. The disclosed subject matteris not limited to any single embodiment described herein, but rathershould be construed in breadth and scope in accordance with the appendedclaims.

What is claimed is:
 1. A computer-implemented method, comprising:identifying one or more computing components associated with a tokenidentifier; identifying a plurality of events associated with the tokenidentifier, wherein the plurality of events are determined from aplurality of messages output by the one or more computing components,and wherein the plurality of events are stored by a data intake andquery system; and generating visualization data for a graphicalvisualization, the visualization data indicating one or more metrics forthe one or more computing components.
 2. The computer-implemented methodof claim 1, wherein the data intake and query system comprises at leastone of: a plurality of event collectors, wherein the plurality of eventcollectors are configured to receive the plurality of events and managestorage of the plurality of events; a plurality of indexers, wherein theplurality of indexers are configured to store the plurality of events;or a plurality of forwarders, wherein the plurality of forwarders areconfigured to receive the plurality of events and forward the pluralityof events for storage.
 3. The computer-implemented method of claim 1,wherein the one or more metrics relate to an operation of the one ormore computing components, and wherein the one or more metrics compriseCPU-related performance metrics, disk-related performance metrics,memory-related performance metrics, network-related performance metrics,energy-usage statistics, data-traffic-related performance metrics,overall system availability performance metrics, cluster-relatedperformance metrics, or virtual machine performance statistics.
 4. Thecomputer-implemented method of claim 1, wherein the token identifiercorresponds to a token, wherein the token comprises metadata for the oneor more computing components.
 5. The computer-implemented method ofclaim 1 further comprising identifying one or more components of thedata intake and query system that process the plurality of events, thevisualization data further indicating one or more metrics for the one ormore components of the data intake and query system.
 6. Thecomputer-implemented method of claim 1, wherein the token identifiercorresponds to a token, wherein the token comprises timestamps for datain the plurality of messages.
 7. The computer-implemented method ofclaim 1, wherein the token identifier is configured to identify a tokenassociated with both the one or more computing components and one ormore components of the data intake and query system.
 8. Thecomputer-implemented method of claim 1, wherein the token identifiercomprises a user-provided token name for a token.
 9. Thecomputer-implemented method of claim 1, wherein the token identifiercorresponds to a token that provides metadata for the plurality ofevents.
 10. A computing system, comprising: memory; and one or moreprocessing devices coupled to the memory and configured to: identify oneor more computing components associated with a token identifier;identify a plurality of events associated with the token identifier,wherein the plurality of events are determined from a plurality ofmessages output by the one or more computing components, and wherein theplurality of events are stored by a data intake and query system; andgenerate visualization data for a graphical visualization, thevisualization data indicating one or more metrics for the one or morecomputing components.
 11. The computing system of claim 10, wherein thedata intake and query system comprises at least one of: a plurality ofevent collectors, wherein the plurality of event collectors areconfigured to receive the plurality of events and manage storage of theplurality of events; a plurality of indexers, wherein the plurality ofindexers are configured to store the plurality of events; or a pluralityof forwarders, wherein the plurality of forwarders are configured toreceive the plurality of events and forward the plurality of events forstorage.
 12. The computing system of claim 10, wherein the visualizationdata relates to a percentage of the plurality of events that weresuccessfully acknowledged.
 13. The computing system of claim 10, whereinthe one or more processing devices are further configured to identifyone or more components of the data intake and query system that processthe plurality of events, the visualization data further indicating oneor more metrics for the one or more components of the data intake andquery system.
 14. The computing system of claim 10, wherein thevisualization data is based on at least one of per-token metrics orsystem-wide metrics.
 15. The computing system of claim 10, wherein theone or more computing components provide the plurality of messages toone or more components of the data intake and query system or the dataintake and query system.
 16. Non-transitory computer readable mediacomprising computer-executable instructions that, when executed by acomputing system, cause the computing system to: identify one or morecomputing components associated with a token identifier; identify aplurality of events associated with the token identifier, wherein theplurality of events are determined from a plurality of messages outputby the one or more computing components, and wherein the plurality ofevents are stored by a data intake and query system; and generatevisualization data for a graphical visualization, the visualization dataindicating one or more metrics for the one or more computing components.17. The non-transitory computer readable media of claim 16, wherein thedata intake and query system comprises at least one of: a plurality ofevent collectors, wherein the plurality of event collectors areconfigured to receive the plurality of events and manage storage of theplurality of events; a plurality of indexers, wherein the plurality ofindexers are configured to store the plurality of events; or a pluralityof forwarders, wherein the plurality of forwarders are configured toreceive the plurality of events and forward the plurality of events forstorage.
 18. The non-transitory computer readable media of claim 16,wherein each of the plurality of messages comprises a tokencorresponding to the token identifier, raw machine data, and eventmetadata.
 19. The non-transitory computer readable media of claim 16,wherein execution of the computer-executable instructions further causesthe computing system to: calculate values associated with thevisualization data; compare the values to a threshold; and invoke aprocess based on comparing the values to the threshold.
 20. Thenon-transitory computer readable media of claim 16, wherein execution ofthe computer-executable instructions further causes the computing systemto identify one or more components of the data intake and query systemthat process the plurality of events, the visualization data furtherindicating one or more metrics for the one or more components of thedata intake and query system.